Interval forecasting of renewable power generation

With the development of renewable energy power generation industry, effective prediction of renewable energy generation is an important issue that modern power grids are facing. Solar power generation is an important part of renewable energy generation. In this project, solar incident radiation (...

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Main Author: Luo, Lingfeng
Other Authors: Xu Yan
Format: Theses and Dissertations
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/78411
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-784112023-07-04T16:18:18Z Interval forecasting of renewable power generation Luo, Lingfeng Xu Yan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution With the development of renewable energy power generation industry, effective prediction of renewable energy generation is an important issue that modern power grids are facing. Solar power generation is an important part of renewable energy generation. In this project, solar incident radiation (SIR) is used as training and test data for research. By using long short term memory (LSTM) to train network parameters, the results of point forecasting are obtained which is then converted into prediction intervals by quantile regression method. Considering the uncertainty of SIR, the prediction interval is more effective than the conventional point forecasting results. Various LSTM framings are used in this project for comparison and analysis. The conclusions have a guiding role in solar power generation prediction Master of Science (Power Engineering) 2019-06-19T13:00:03Z 2019-06-19T13:00:03Z 2019 Thesis http://hdl.handle.net/10356/78411 en 58 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
Luo, Lingfeng
Interval forecasting of renewable power generation
description With the development of renewable energy power generation industry, effective prediction of renewable energy generation is an important issue that modern power grids are facing. Solar power generation is an important part of renewable energy generation. In this project, solar incident radiation (SIR) is used as training and test data for research. By using long short term memory (LSTM) to train network parameters, the results of point forecasting are obtained which is then converted into prediction intervals by quantile regression method. Considering the uncertainty of SIR, the prediction interval is more effective than the conventional point forecasting results. Various LSTM framings are used in this project for comparison and analysis. The conclusions have a guiding role in solar power generation prediction
author2 Xu Yan
author_facet Xu Yan
Luo, Lingfeng
format Theses and Dissertations
author Luo, Lingfeng
author_sort Luo, Lingfeng
title Interval forecasting of renewable power generation
title_short Interval forecasting of renewable power generation
title_full Interval forecasting of renewable power generation
title_fullStr Interval forecasting of renewable power generation
title_full_unstemmed Interval forecasting of renewable power generation
title_sort interval forecasting of renewable power generation
publishDate 2019
url http://hdl.handle.net/10356/78411
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